In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

In this paper we provide a review of the recent literature on Bayesian Blind Image
Deconvolution (BID) methods. We believe that two events have marked the recent history of …

For many centuries, people have tried to learn about the state of their health. Initially, in the
pre-technological period, they had to rely only on their senses. Then there were simple tools …

In this paper, we address the super resolution (SR) problem from a set of degraded low
resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the …

How to recover a clear image from a single motion-blurred image has long been a
challenging open problem in digital imaging. In this paper, we focus on how to recover a …

A method for blind image deblurring is presented. The method only makes weak
assumptions about the blurring filter and is able to undo a wide variety of blurring …

Regularized iterative reconstruction algorithms for imaging inverse problems require
selection of appropriate regularization parameter values. We focus on the challenging …

Real-world single image denoising is crucial and practical in computer vision. Bayesian
inversions combined with score priors now have proven effective for single image denoising …

Tactile Internet with Human-in-the-Loop describes the change from the current Internet,
which focuses on the democratization of information independent of location or time, to the …

There are two key issues in successfully solving the image restoration problem: 1)
estimation of the regularization parameter that balances data fidelity with the regularity of the …